Dataset assetOpen Source CommunityTime Series ForecastingTime Series Data

ETTm2, ETTh1, ETTh2, M4, Stock

This repository contains multiple time‑series datasets for testing and training time‑series forecasting models, including ETTm2, ETTh1, ETTh2, M4, and Stock datasets.

Source

github

Created

Oct 31, 2024

Updated

Oct 31, 2024

Signals

1,258 views

Availability

Linked source ready

Overview

Dataset description and usage context

Dataset Overview

Dataset Download

The datasets can be downloaded from Google Drive and extracted to the datasets/ directory.

Dataset Usage

The datasets are used for time‑series forecasting experiments, supporting the following models:
- TSMixer
- DLinear
- PatchTST
- TimesNet

Experiment Examples

Long‑term Forecasting

Example: use DLinear on the ETTm2 dataset for a forecast length of 96:

python main.py --model DLinear  --data ETTm2 --out_len 96 --in_len 336 --learning_rate 0.001 --batch_size 32 --individual "c"

Zero‑Shot Evaluation

Example: train DLinear on ETTh1 and perform zero‑shot testing on ETTh2:

python main.py --zero_shot_test True --data ETTh1 --test_data ETTh2 --model DLinear --out_len 96 --individual "c"

M4 Forecasting

In the M4 dataset, input length and forecast length are specified in datasets/data_loader.py. Train DLinear:

python main_m4.py --model DLinear --data m4 --batch_size 32 --individual "c"

Stock Price Forecasting

Train DLinear on the Stock dataset with a forecast length of 7:

python main_stock.py --model DLinear --data stock  --out_len 7 --in_len 28 --batch_size 128 --individual "c"

Citation

If you use this dataset, please cite the related paper:

@article{chen2024similarity,
  title={From Similarity to Superiority: Channel Clustering for Time Series Forecasting},
  author={Chen, Jialin and Lenssen, Jan Eric and Feng, Aosong and Hu, Weihua and Fey, Matthias and Tassiulas, Leandros and Leskovec, Jure and Ying, Rex},
  journal={arXiv preprint arXiv:2404.01340},
  year={2024}
}

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